{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "da-rnn.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NK8TN-slic4",
        "colab_type": "text"
      },
      "source": [
        "# DA RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOjT-NCQ8SOI",
        "colab_type": "text"
      },
      "source": [
        "Google Drive Mount"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_vN_3e5w-cy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUV-YBZixIYi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cd drive/My\\ Drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PE43ff9_xLWy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cd 'Colab Notebooks'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md29RMu5xT07",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRWBLu5pmYQb",
        "colab_type": "text"
      },
      "source": [
        "## Hyper-parameters settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K1hkpR8mbd1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataroot = 'nasdaq100_padding.csv'\n",
        "\n",
        "batchsize = 128\n",
        "nhidden_encoder = 128\n",
        "nhidden_decoder = 128\n",
        "ntimestep = 10\n",
        "lr = 0.001\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFX-OX3e8cMW",
        "colab_type": "text"
      },
      "source": [
        "## Model Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtEHHVWxlh0C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "\n",
        "\n",
        "class Encoder(nn.Module):\n",
        "    \"\"\"encoder in DA_RNN.\"\"\"\n",
        "\n",
        "    def __init__(self, T,\n",
        "                 input_size,\n",
        "                 encoder_num_hidden,\n",
        "                 parallel=False):\n",
        "        \"\"\"Initialize an encoder in DA_RNN.\"\"\"\n",
        "        super(Encoder, self).__init__()\n",
        "        self.encoder_num_hidden = encoder_num_hidden\n",
        "        self.input_size = input_size\n",
        "        self.parallel = parallel\n",
        "        self.T = T\n",
        "\n",
        "        # Fig 1. Temporal Attention Mechanism: Encoder is LSTM\n",
        "        self.encoder_lstm = nn.LSTM(\n",
        "            input_size=self.input_size,\n",
        "            hidden_size=self.encoder_num_hidden,\n",
        "            num_layers = 1\n",
        "        )\n",
        "\n",
        "        # Construct Input Attention Mechanism via deterministic attention model\n",
        "        # Eq. 8: W_e[h_{t-1}; s_{t-1}] + U_e * x^k\n",
        "        self.encoder_attn = nn.Linear(\n",
        "            in_features=2 * self.encoder_num_hidden + self.T - 1,\n",
        "            out_features=1\n",
        "        )\n",
        "\n",
        "    def forward(self, X):\n",
        "        \"\"\"forward.\n",
        "\n",
        "        Args:\n",
        "            X: input data\n",
        "\n",
        "        \"\"\"\n",
        "        X_tilde = Variable(X.data.new(\n",
        "            X.size(0), self.T - 1, self.input_size).zero_())\n",
        "        X_encoded = Variable(X.data.new(\n",
        "            X.size(0), self.T - 1, self.encoder_num_hidden).zero_())\n",
        "\n",
        "        # Eq. 8, parameters not in nn.Linear but to be learnt\n",
        "        # v_e = torch.nn.Parameter(data=torch.empty(\n",
        "        #     self.input_size, self.T).uniform_(0, 1), requires_grad=True)\n",
        "        # U_e = torch.nn.Parameter(data=torch.empty(\n",
        "        #     self.T, self.T).uniform_(0, 1), requires_grad=True)\n",
        "\n",
        "        # h_n, s_n: initial states with dimention hidden_size\n",
        "        h_n = self._init_states(X)\n",
        "        s_n = self._init_states(X)\n",
        "\n",
        "        for t in range(self.T - 1):\n",
        "            # batch_size * input_size * (2 * hidden_size + T - 1)\n",
        "            x = torch.cat((h_n.repeat(self.input_size, 1, 1).permute(1, 0, 2),\n",
        "                           s_n.repeat(self.input_size, 1, 1).permute(1, 0, 2),\n",
        "                           X.permute(0, 2, 1)), dim=2)\n",
        "\n",
        "            x = self.encoder_attn(\n",
        "                x.view(-1, self.encoder_num_hidden * 2 + self.T - 1))\n",
        "\n",
        "            # get weights by softmax\n",
        "            alpha = F.softmax(x.view(-1, self.input_size))\n",
        "\n",
        "            # get new input for LSTM\n",
        "            x_tilde = torch.mul(alpha, X[:, t, :])\n",
        "\n",
        "            # Fix the warning about non-contiguous memory\n",
        "            # https://discuss.pytorch.org/t/dataparallel-issue-with-flatten-parameter/8282\n",
        "            self.encoder_lstm.flatten_parameters()\n",
        "\n",
        "            # encoder LSTM\n",
        "            _, final_state = self.encoder_lstm(x_tilde.unsqueeze(0), (h_n, s_n))\n",
        "            h_n = final_state[0]\n",
        "            s_n = final_state[1]\n",
        "\n",
        "            X_tilde[:, t, :] = x_tilde\n",
        "            X_encoded[:, t, :] = h_n\n",
        "\n",
        "        return X_tilde, X_encoded\n",
        "\n",
        "    def _init_states(self, X):\n",
        "        \"\"\"Initialize all 0 hidden states and cell states for encoder.\n",
        "\n",
        "        Args:\n",
        "            X\n",
        "\n",
        "        Returns:\n",
        "            initial_hidden_states\n",
        "        \"\"\"\n",
        "        # https://pytorch.org/docs/master/nn.html?#lstm\n",
        "        return Variable(X.data.new(1, X.size(0), self.encoder_num_hidden).zero_())\n",
        "\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    \"\"\"decoder in DA_RNN.\"\"\"\n",
        "\n",
        "    def __init__(self, T, decoder_num_hidden, encoder_num_hidden):\n",
        "        \"\"\"Initialize a decoder in DA_RNN.\"\"\"\n",
        "        super(Decoder, self).__init__()\n",
        "        self.decoder_num_hidden = decoder_num_hidden\n",
        "        self.encoder_num_hidden = encoder_num_hidden\n",
        "        self.T = T\n",
        "\n",
        "        self.attn_layer = nn.Sequential(\n",
        "            nn.Linear(2 * decoder_num_hidden + encoder_num_hidden, encoder_num_hidden),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(encoder_num_hidden, 1)\n",
        "        )\n",
        "        self.lstm_layer = nn.LSTM(\n",
        "            input_size=1,\n",
        "            hidden_size=decoder_num_hidden\n",
        "        )\n",
        "        self.fc = nn.Linear(encoder_num_hidden + 1, 1)\n",
        "        self.fc_final = nn.Linear(decoder_num_hidden + encoder_num_hidden, 1)\n",
        "\n",
        "        self.fc.weight.data.normal_()\n",
        "\n",
        "    def forward(self, X_encoded, y_prev):\n",
        "        \"\"\"forward.\"\"\"\n",
        "        d_n = self._init_states(X_encoded)\n",
        "        c_n = self._init_states(X_encoded)\n",
        "\n",
        "        for t in range(self.T - 1):\n",
        "\n",
        "            x = torch.cat((d_n.repeat(self.T - 1, 1, 1).permute(1, 0, 2),\n",
        "                           c_n.repeat(self.T - 1, 1, 1).permute(1, 0, 2),\n",
        "                           X_encoded), dim=2)\n",
        "\n",
        "            beta = F.softmax(self.attn_layer(\n",
        "                x.view(-1, 2 * self.decoder_num_hidden + self.encoder_num_hidden)).view(-1, self.T - 1))\n",
        "\n",
        "            # Eqn. 14: compute context vector\n",
        "            # batch_size * encoder_hidden_size\n",
        "            context = torch.bmm(beta.unsqueeze(1), X_encoded)[:, 0, :]\n",
        "            if t < self.T - 1:\n",
        "                # Eqn. 15\n",
        "                # batch_size * 1\n",
        "                y_tilde = self.fc(\n",
        "                    torch.cat((context, y_prev[:, t].unsqueeze(1)), dim=1))\n",
        "\n",
        "                # Eqn. 16: LSTM\n",
        "                self.lstm_layer.flatten_parameters()\n",
        "                _, final_states = self.lstm_layer(\n",
        "                    y_tilde.unsqueeze(0), (d_n, c_n))\n",
        "\n",
        "                d_n = final_states[0]  # 1 * batch_size * decoder_num_hidden\n",
        "                c_n = final_states[1]  # 1 * batch_size * decoder_num_hidden\n",
        "\n",
        "        # Eqn. 22: final output\n",
        "        y_pred = self.fc_final(torch.cat((d_n[0], context), dim=1))\n",
        "\n",
        "        return y_pred\n",
        "\n",
        "    def _init_states(self, X):\n",
        "        \"\"\"Initialize all 0 hidden states and cell states for encoder.\n",
        "\n",
        "        Args:\n",
        "            X\n",
        "        Returns:\n",
        "            initial_hidden_states\n",
        "\n",
        "        \"\"\"\n",
        "        # hidden state and cell state [num_layers*num_directions, batch_size, hidden_size]\n",
        "        # https://pytorch.org/docs/master/nn.html?#lstm\n",
        "        return Variable(X.data.new(1, X.size(0), self.decoder_num_hidden).zero_())\n",
        "\n",
        "\n",
        "class DA_rnn(nn.Module):\n",
        "    \"\"\"da_rnn.\"\"\"\n",
        "\n",
        "    def __init__(self, X, y, T,\n",
        "                 encoder_num_hidden,\n",
        "                 decoder_num_hidden,\n",
        "                 batch_size,\n",
        "                 learning_rate,\n",
        "                 epochs,\n",
        "                 parallel=False):\n",
        "        \"\"\"da_rnn initialization.\"\"\"\n",
        "        super(DA_rnn, self).__init__()\n",
        "        self.encoder_num_hidden = encoder_num_hidden\n",
        "        self.decoder_num_hidden = decoder_num_hidden\n",
        "        self.learning_rate = learning_rate\n",
        "        self.batch_size = batch_size\n",
        "        self.parallel = parallel\n",
        "        self.shuffle = False\n",
        "        self.epochs = epochs\n",
        "        self.T = T\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "\n",
        "        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
        "        print(\"==> Use accelerator: \", self.device)\n",
        "\n",
        "        self.Encoder = Encoder(input_size=X.shape[1],\n",
        "                               encoder_num_hidden=encoder_num_hidden,\n",
        "                               T=T).to(self.device)\n",
        "        self.Decoder = Decoder(encoder_num_hidden=encoder_num_hidden,\n",
        "                               decoder_num_hidden=decoder_num_hidden,\n",
        "                               T=T).to(self.device)\n",
        "\n",
        "        # Loss function\n",
        "        self.criterion = nn.MSELoss()\n",
        "\n",
        "        if self.parallel:\n",
        "            self.encoder = nn.DataParallel(self.encoder)\n",
        "            self.decoder = nn.DataParallel(self.decoder)\n",
        "\n",
        "        self.encoder_optimizer = optim.Adam(params=filter(lambda p: p.requires_grad,\n",
        "                                                          self.Encoder.parameters()),\n",
        "                                            lr=self.learning_rate)\n",
        "        self.decoder_optimizer = optim.Adam(params=filter(lambda p: p.requires_grad,\n",
        "                                                          self.Decoder.parameters()),\n",
        "                                            lr=self.learning_rate)\n",
        "\n",
        "        # Training set\n",
        "        self.train_timesteps = int(self.X.shape[0] * 0.7)\n",
        "        self.y = self.y - np.mean(self.y[:self.train_timesteps])\n",
        "        self.input_size = self.X.shape[1]\n",
        "\n",
        "    def train(self):\n",
        "        \"\"\"training process.\"\"\"\n",
        "        iter_per_epoch = int(np.ceil(self.train_timesteps * 1. / self.batch_size))\n",
        "        self.iter_losses = np.zeros(self.epochs * iter_per_epoch)\n",
        "        self.epoch_losses = np.zeros(self.epochs)\n",
        "\n",
        "        n_iter = 0\n",
        "\n",
        "        for epoch in range(self.epochs):\n",
        "            if self.shuffle:\n",
        "                ref_idx = np.random.permutation(self.train_timesteps - self.T)\n",
        "            else:\n",
        "                ref_idx = np.array(range(self.train_timesteps - self.T))\n",
        "\n",
        "            idx = 0\n",
        "\n",
        "            while (idx < self.train_timesteps):\n",
        "                # get the indices of X_train\n",
        "                indices = ref_idx[idx:(idx + self.batch_size)]\n",
        "                # x = np.zeros((self.T - 1, len(indices), self.input_size))\n",
        "                x = np.zeros((len(indices), self.T - 1, self.input_size))\n",
        "                y_prev = np.zeros((len(indices), self.T - 1))\n",
        "                y_gt = self.y[indices + self.T]\n",
        "\n",
        "                # format x into 3D tensor\n",
        "                for bs in range(len(indices)):\n",
        "                    x[bs, :, :] = self.X[indices[bs]:(indices[bs] + self.T - 1), :]\n",
        "                    y_prev[bs, :] = self.y[indices[bs]: (indices[bs] + self.T - 1)]\n",
        "\n",
        "                loss = self.train_forward(x, y_prev, y_gt)\n",
        "                self.iter_losses[int(epoch * iter_per_epoch + idx / self.batch_size)] = loss\n",
        "\n",
        "                idx += self.batch_size\n",
        "                n_iter += 1\n",
        "\n",
        "                if n_iter % 10000 == 0 and n_iter != 0:\n",
        "                    for param_group in self.encoder_optimizer.param_groups:\n",
        "                        param_group['lr'] = param_group['lr'] * 0.9\n",
        "                    for param_group in self.decoder_optimizer.param_groups:\n",
        "                        param_group['lr'] = param_group['lr'] * 0.9\n",
        "\n",
        "                self.epoch_losses[epoch] = np.mean(self.iter_losses[range(\n",
        "                    epoch * iter_per_epoch, (epoch + 1) * iter_per_epoch)])\n",
        "\n",
        "            if epoch % 10 == 0:\n",
        "                print(\"Epochs: \", epoch, \" Iterations: \", n_iter,\n",
        "                      \" Loss: \", self.epoch_losses[epoch])\n",
        "\n",
        "            if epoch % 10 == 0:\n",
        "                y_train_pred = self.test(on_train=True)\n",
        "                y_test_pred = self.test(on_train=False)\n",
        "                y_pred = np.concatenate((y_train_pred, y_test_pred))\n",
        "                plt.ioff()\n",
        "                plt.figure()\n",
        "                plt.plot(range(1, 1 + len(self.y)), self.y, label=\"True\")\n",
        "                plt.plot(range(self.T, len(y_train_pred) + self.T),\n",
        "                         y_train_pred, label='Predicted - Train')\n",
        "                plt.plot(range(self.T + len(y_train_pred), len(self.y) + 1),\n",
        "                         y_test_pred, label='Predicted - Test')\n",
        "                plt.legend(loc='upper left')\n",
        "                plt.show()\n",
        "\n",
        "            # # Save files in last iterations\n",
        "            # if epoch == self.epochs - 1:\n",
        "            #     np.savetxt('../loss.txt', np.array(self.epoch_losses), delimiter=',')\n",
        "            #     np.savetxt('../y_pred.txt',\n",
        "            #                np.array(self.y_pred), delimiter=',')\n",
        "            #     np.savetxt('../y_true.txt',\n",
        "            #                np.array(self.y_true), delimiter=',')\n",
        "\n",
        "    def train_forward(self, X, y_prev, y_gt):\n",
        "        \"\"\"\n",
        "        Forward pass.\n",
        "\n",
        "        Args:\n",
        "            X:\n",
        "            y_prev:\n",
        "            y_gt: Ground truth label\n",
        "\n",
        "        \"\"\"\n",
        "        # zero gradients\n",
        "        self.encoder_optimizer.zero_grad()\n",
        "        self.decoder_optimizer.zero_grad()\n",
        "\n",
        "        input_weighted, input_encoded = self.Encoder(\n",
        "            Variable(torch.from_numpy(X).type(torch.FloatTensor).to(self.device)))\n",
        "        y_pred = self.Decoder(input_encoded, Variable(\n",
        "            torch.from_numpy(y_prev).type(torch.FloatTensor).to(self.device)))\n",
        "\n",
        "        y_true = Variable(torch.from_numpy(\n",
        "            y_gt).type(torch.FloatTensor).to(self.device))\n",
        "\n",
        "        y_true = y_true.view(-1, 1)\n",
        "        loss = self.criterion(y_pred, y_true)\n",
        "        loss.backward()\n",
        "\n",
        "        self.encoder_optimizer.step()\n",
        "        self.decoder_optimizer.step()\n",
        "\n",
        "        return loss.item()\n",
        "\n",
        "\n",
        "    def test(self, on_train=False):\n",
        "        \"\"\"test.\"\"\"\n",
        "\n",
        "        if on_train:\n",
        "            y_pred = np.zeros(self.train_timesteps - self.T + 1)\n",
        "        else:\n",
        "            y_pred = np.zeros(self.X.shape[0] - self.train_timesteps)\n",
        "\n",
        "        i = 0\n",
        "        while i < len(y_pred):\n",
        "            batch_idx = np.array(range(len(y_pred)))[i: (i + self.batch_size)]\n",
        "            X = np.zeros((len(batch_idx), self.T - 1, self.X.shape[1]))\n",
        "            y_history = np.zeros((len(batch_idx), self.T - 1))\n",
        "\n",
        "            for j in range(len(batch_idx)):\n",
        "                if on_train:\n",
        "                    X[j, :, :] = self.X[range(\n",
        "                        batch_idx[j], batch_idx[j] + self.T - 1), :]\n",
        "                    y_history[j, :] = self.y[range(\n",
        "                        batch_idx[j], batch_idx[j] + self.T - 1)]\n",
        "                else:\n",
        "                    X[j, :, :] = self.X[range(\n",
        "                        batch_idx[j] + self.train_timesteps - self.T, batch_idx[j] + self.train_timesteps - 1), :]\n",
        "                    y_history[j, :] = self.y[range(\n",
        "                        batch_idx[j] + self.train_timesteps - self.T, batch_idx[j] + self.train_timesteps - 1)]\n",
        "\n",
        "            y_history = Variable(torch.from_numpy(\n",
        "                y_history).type(torch.FloatTensor).to(self.device))\n",
        "            _, input_encoded = self.Encoder(\n",
        "                Variable(torch.from_numpy(X).type(torch.FloatTensor).to(self.device)))\n",
        "            y_pred[i:(i + self.batch_size)] = self.Decoder(input_encoded,\n",
        "                                                           y_history).cpu().data.numpy()[:, 0]\n",
        "            i += self.batch_size\n",
        "\n",
        "        return y_pred\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7v_B_SV8iHu",
        "colab_type": "text"
      },
      "source": [
        "## Util Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoFUkg_Elh-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "def read_data(input_path, debug=True):\n",
        "    \"\"\"Read nasdaq stocks data.\n",
        "\n",
        "    Args:\n",
        "        input_path (str): directory to nasdaq dataset.\n",
        "\n",
        "    Returns:\n",
        "        X (np.ndarray): features.\n",
        "        y (np.ndarray): ground truth.\n",
        "\n",
        "    \"\"\"\n",
        "    df = pd.read_csv(input_path, nrows=250 if debug else None)\n",
        "    # X = df.iloc[:, 0:-1].values\n",
        "    X = df.loc[:, [x for x in df.columns.tolist() if x != 'NDX']].as_matrix()\n",
        "    # y = df.iloc[:, -1].values\n",
        "    y = np.array(df.NDX)\n",
        "\n",
        "    return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca-OxO-Z8lEt",
        "colab_type": "text"
      },
      "source": [
        "## Main"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jc-8Qvsyv6tt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "09ee121b-2978-4f75-e826-e2d5fab5bbb9"
      },
      "source": [
        "# Read dataset\n",
        "print(\"==> Load dataset ...\")\n",
        "X, y = read_data(dataroot, debug=False)\n",
        "\n",
        "# Initialize model\n",
        "print(\"==> Initialize DA-RNN model ...\")\n",
        "model = DA_rnn(\n",
        "    X,\n",
        "    y,\n",
        "    ntimestep,\n",
        "    nhidden_encoder,\n",
        "    nhidden_decoder,\n",
        "    batchsize,\n",
        "    lr,\n",
        "    epochs\n",
        ")\n",
        "\n",
        "# Train\n",
        "print(\"==> Start training ...\")\n",
        "model.train()\n",
        "\n",
        "# Prediction\n",
        "y_pred = model.test()\n",
        "\n",
        "fig1 = plt.figure()\n",
        "plt.semilogy(range(len(model.iter_losses)), model.iter_losses)\n",
        "plt.savefig(\"1.png\")\n",
        "plt.close(fig1)\n",
        "\n",
        "fig2 = plt.figure()\n",
        "plt.semilogy(range(len(model.epoch_losses)), model.epoch_losses)\n",
        "plt.savefig(\"2.png\")\n",
        "plt.close(fig2)\n",
        "\n",
        "fig3 = plt.figure()\n",
        "plt.plot(y_pred, label='Predicted')\n",
        "plt.plot(model.y[model.train_timesteps:], label=\"True\")\n",
        "plt.legend(loc='upper left')\n",
        "plt.savefig(\"3.png\")\n",
        "plt.close(fig3)\n",
        "print('Finished Training')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==> Load dataset ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:72: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:140: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "==> Initialize DA-RNN model ...\n",
            "==> Use accelerator:  cuda:0\n",
            "==> Start training ...\n",
            "Epochs:  0  Iterations:  222  Loss:  2128.0527825414597\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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nin1e8tlbna2e/eWw+fW2Z0ZAJiOCc365Mw8AzC0bAYsxICP2+zUwfKz8CSER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\nU1zlIADHT2QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epochs:  10  Iterations:  2442  Loss:  43.68726467871451\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NxXsfVRtDajBVtU7WLnhymzRKBQCM4sXaAHEVHqaX1leiePmLWKVJxyP87xg/\nf4dn57tAsQBNmDN54U5LHp9GmsZ4c+D7AAAKir2XMhyep5VEPXtv91H6zsqpuH7SOd5aJSw22nX+\np3AiWJr/9ZTSNEppH/N+OoD1lNL2ANab9xkMjzCaKOJJlSShmq3Z5yXDff65Dtx7oGQ0myhr42rk\nGqJTdn6GVkcX4GruLJ5T/4iD5x2bNbxhwFvr/TqeI8prjNh4XNkLx1O2ZRcCRHSrHJg4GS3iUgCI\nZp8L5dLgrt/HWBd5DSaCng1vBgCoOO/fCI0K8RFpLcXFXp5YTT+dEjp5fY1QECqzzxgAi83biwGM\nddKXwVDEYBBfx22LsBCbm1xvDmN54gd5FS5PkNXifeqIdfuWjyyblMgfEi7S0MuxEzSdyFkPB3CO\nKkgJ357+aRfu/+5PSdpsb1HFZyK23XsAgPPF5eDN6xsCFXDsijSBn22QVePYaDTWpgIA9pz2vpjN\npuNyF+HmDcTo3rhoq9np2pRrvb5GKAiG8KcA1hBC9hJCZpjbmlBKa32v8gA0UTqREDKDEJJBCMko\nKPB/hj5GZGPQKwRRKbhqLt/vW+h/sv3iaYMW1u0+9+KocBUAQFDQLgWFB4JT7Oa/Wuvfl+LBnP9y\n2ztjb80HiGn7ISoNvpuZolostWxnVn0J3ry+QUGREtNG0te2kpaaU1liQF5Z7v3n/vuE/A2mqFqU\nR0nRVot1Q11kuX4GQ/gPppT2AjAawKOEkKG2B6mYIFtRP6KULqCU9qGU9klOTg7CVBmRhKFGwcaq\nIICTUYwiGovfTANdD5o6xOGhUioP6gGAduQ8AKBCYS3VRD2zNV8sc09Ybs++jNT0FR6NDQCLNO96\nfI436NVZAIByvdzryBN+2iOPyq4V8BuP5yP7ovT7tTXvaHgViFn4c1rlAD93ICq5t0/35O4AgLTG\nylleI4GAC39K6Xnz//kAlgHoB+ASIaQZAJj/949xkFGvWJaRI28kBJtu2iBp2qN7BAmkHDp7840S\nLXpL90e8YdmMJ8omjNpC7ftzrf7gx/uKqQDs6wK4YpeCF4kSb65SrlfgDjtOFkoqYwWSvCrHKTXc\n4blfDkj2Z/afCZ6rFVsUfx9y/Gal4dW4UCz+zZRy/7hLTJuPZG1dksQU4je1vgkAkN4v8pYtAyr8\nCSExhJC42m0AIwAcArAcwHRzt+kAlJOqMxhOKK0Qb+zfVKMk7SqzcCB2L5S1HjgAgHFfKg/a5Xbp\n/sDH3Z6P7cKwIdrsY66UXdMJnH0hdgccOl+KWHhnT5/45U5M/So4Pv8XKnxbs9A2lr7djGo9ylIq\nUdv4L+tCcKOpuLfLvZLcOoNdGEUAACAASURBVDzHY09OEQJBnEa0+ceoY3Bw+kFMvnpyQK4TSAKt\n+TcBsJUQsh/AbgArKKWrAbwN4EZCSBaA4eZ9BsMjmhjN5paoFpJ2NV8r/J2giVFub54GtBrk+uIj\n3wIm/SRpasNZUwjozTm/Csocu/9tPlGALVnStaz2nDQFw16hPZS4hduBQzonfuUpfR0fg1jzNhjE\naxr4dL4mcYtkP0YVI7Hrx6R+DgC4sX0anu7zNAgh0PFiWmWOcIiJCkxcg5pz7yEdzgRU+FNKT1FK\ne5j/daGUzja3F1JKh1FK21NKh1NKA/N4ZkQ0pdUGHHLi6vhwzr8BAJPLpJWbnPr6v5ALDP8P0GG0\n4z7DzfV8G8qLxFgY8AjQYaSk6Q7eKqgyz4smoI/XHlM8nVKKaV/vlmngZ7kUy3Yl1aI9ycVdX+zA\n0YulkmphczWfOp4bAEz/E3gmS/HQt+rZ6EJOKx7zNw21/ktxvGXCFvAcb3mzs+WDfbMs2xM7iS63\nGl4DdYNMv12/rsEifBlhy/Svd+OWT7c6tE9fjBH9qqvsFt3UKmWzDwBAGwcM/jfAcbLF4ZpmZm05\nuaOYLmH0O17PXceJtv4kUoILxVWy445cQBs3smrK0aQG8aQKu08XYfTHW3DTJ1uUT1JCrQNilesO\nD+YPY4X2JeRe8d0N0xXUj3WMa71pOIXMq32b9rFsP9X7Keybug8qTgUj8TDOoh7BhD8jbNl3Vrxx\njQ4kJY0WPcCInYudilf+WZ8dtVjaYFf0PX/4J+KGLh549QrQUXw7EJ486NG8AaBL8SYAwPvqL2BS\nmL9AKXJ0k5CjmwTB5nisWp4jvgGs+YH+88dhHMvzwIPm6aPAI7uAKfLkZxuOBcPPwv8LyzwnN+jN\nGjjLsk0IsXj9RKGZrK+3XNPsGr+NFQ4w4c/wiXdWH8N//jgckLFTSAHGcZthMClXxDqcOAIAUD7w\nWUm7mheFAwGVZPTidXaumskdJbtlNcqeObULjIhR1qQtXPeCZTO1sej/HUeqgCp5Hn3BpiB5ud66\nKCzYlWMEgP26GZjMr8Nd/EZ8sy0Hoz6yewOY5SQKOL450LgT0FoegHSpJAiafwCqmSnlzK+189sz\nzInrrqfc2+Vev40VDjDhHyaUVRuQmr4CKxxUJQpHaowmfL7pJL7ZlhOQ8Vdr0jFHMx81NcqLdrUV\nnHKrpTe+ynbB18bVMibGeRWrhCgHt0OtAItykYKqmdX8pNNaFwRppTy61NaUZZv8TeCVYwlmq7/G\nu2oHHkruwKtR2Eq6zjG9yMW6gR/wl9lHDecBVNFq5e9t1pAn/HJ9ABjUwg1HgAiCCf8wIPdKJbrN\nWgMAePR773KuVxtMePZ/+1GgkIEwUFwoDmwiq1gi2sr1BuXPpDKX0TPZ5d7hzFG1BBQQrA8O7cW9\nTq/XNMbBQnHDq4Br04FJPzqYaFPxf5sFYL7S6k1DzQ+gIe9uwIwl5kRkNsK/pNyqgddEOQ9mzNFN\nwo+a15z2ccT5DtMl+5qqyDH7xKi8Kz/J20RM+yPX0OJRi/HzrT+77hgBMOEfBszdkO3zGMv3X8D/\n9ubiMS8fHt6g4gi+V7+B5ZqXAnodvZ3mv/7oJXy6Pgsqc3DVVcnSlMu1aZ0JKErKrYutUS26Or9Q\nhQPhQAhw/QtAQmvl408dAl7Ol6RVFnrdY51Piejrfq6oCmuOiJGmtsFfpeXWCFLbReo3DMq+49dw\nNh5Ezkw+dhjs1h4qG3V00NN/mJwUXvcEFXHgmguggdY9d9J7v9njupMLejXphY4Jgf/eggET/mHA\n0j3nEIdKvKpajM4kx6voy1qNf9fp4HnN8oYyDOSPoDsnug0aHdjmfUVvl7P9/sUZ+GDtCUuSsmid\n1E7OmW3+HATo9TaeNtEu3A4TlX3qXcKrAZV0DpqmV1u2SaXcp972b1xcofxmU1sM3l+Y7L7H0maD\n/Tq+EtsvbvLLOA21cZL93oli/d4Yrhk2jN+gdIoFY2UqBH2iX+ZRl2DCPwxoRfJwUPcA7lX9hZXa\nF70y3VSVFuF/mlloRRxXovI7JdaApB0nC9HupVXYFoDgIZNCPVoAlsIrnF1FJc7s5/+aejE423Nr\nzTMK5CcPAOL95xkCtTXStCq+LQDggPYBLNW8DkAq/JftzZGdnjVyCfpwyn763hLHSYW/x0nnvKBH\nUj+/jMOrpe6yEzqLJrZ+Lbq4rKhFjTHgNIVQN9rql7nUFZjwDwMSIK0R+lOG5/lQLmf8gr7cCazR\nPO+vabnEVGWd98QvdwIAZi33v+ePJk+aknkivx4fqudhgmoTAIDYVe+ytfOqqI3wj1NMHgsAMKji\nHB7zCpsFyBN5JbhcXoN4Uon+nJiTx9YLpmW8PFpU4KPA+zn98tWN7TyJAuCJc66oErOWH4agF9+y\nkqMcf+eeQKlUAeDM7p4qBbdPewgnmg3ti7C7f22CrjF1L+s8E/5hwDLtq5L999cc93iMie3EH7iW\nGFAahFJ9lFLoy6VeLKnkoqIPtq8YuSjJ/lvqr3A7v82yz6vshL/Nw6BKKfOnAhz183emtnogfb31\nFPq8Ia0QZuvqOfrqIBWyswtqC0Rqt6d+zMSi7TkgvBib4K8Ecg/1mCHZj1aJD9d4jesSm6rYE5Zt\nT9+qxRgMgvLq4NRBCCZM+Ichkzt5XnWox2lrioNTBfIUtP7mxWWH8OkKawm9EdwebNL+H97v4H22\nSQDYmnVZVjbvssH5a719MjTeZuE187R7qXw5IXAPTGKnYVfpTZIQ38ZZDryIrnvRvxPRSRdGy6sc\n17/1lowzYkwD4cWxPS5mY8OlUuuDu29Taa6iwS0G4/m+z+PZvs/an+aUh7/1bB3lyEUxoO74pXIX\nPSMPJvzDkGncSp/OHztvGzYGOHrzh91n0ZBYb4gFmg8BAJ0zXvZ6zPyyakz5ahemfrVL0j77T+eF\nODh7s4/KavaZt9Y9M5RB7UGRdjfJSBwDAOjFZSEB1qjch77dCwrrA2FD5gmcLDB/l7aa8nXPAyNm\nOxy/ZOgszyaUKl3gnb/5pGfnOyGvpBqF5TXgo08ittMLNke8My39tu88rnnTWnLS3o+fEIIpnacg\nRu3YC0iJvWfkAXfO0JsEABTwsC5DJMCEfxhwQJC6EHY8tdhBT8ccaf+wZTseFbh3ke9ubc6haETk\n2hAneK9N7j8nui3WpnWoRQUTvt15Bvc7+EyEc2zzj4J7r/m5V93qyVTdokNz0e79gvoH/KOz/n12\nny6UpHTYKKTJF8pr314GPubQndPQ5kaf5nezaSNWHvRPUGH/t9aj9xvrEN3qSxBi/WyCl2aff/8o\nTcjG+2lxmqg9c0h4Z81uEEKhbuhfz6twgAn/MKDWVbKqwxivx6jSJlm2+3Cerxl4So5uMp5U/Spr\nr45y7FHjigeXZGAkt0eSywYAhnAH8fJvh7De0duMXbg/b5PVs3aB1RXN4vyfojdWr1x6tDFfLlls\nnaP+HERvtpE7GOsfTW9ZW4OYKIWe7nMnvxmvfLfRq4pg7uJLhC+nsf69/SX8o1su8qj/vvz94lzU\n7sdTRApM+IcR5Te+DwDY1963kPSvNe/jOdVS1x0DACd4H2HcFIX4QvMhNmv/LWl/Wv0zxnGbMVf9\niVvj2Ar/p9XuRWO2auh8XcEbiFqepwcAVuExyUJoAilH6/PLpefanUOJfB1IrfJ8bcieDN2/fB6j\nvMYIor4MolLKYeTdmJrkvxDTdo5lXymfjzdwWs80f3Xj5a47RSi+/3oYfoOoRO1TUEhZ6wp7/eoR\n1XKcLChH22TvwuKdUW0wQTmNFsCZvBf+w3jRpbOBQrnEOZr5bo/DKeR7d4mDWAJfIMNeAQ7Js2nG\noBpVdlJx3eGL6FDq2DNJ0SdfoV6xNzzE/wHgZq/PLyyvQWy79xWPUS9t/tqkjV7Px58QPvDJ70IF\n0/zDCL5W6HugLo38cDPmbshSdKkb/cE6hTO843xxlSUvfaeZqx3244ze5/v5l8qqZV2pUF47UEqP\nbA/hvDARJHXw/BxXxKc4PCTYeQDxMGF/rjPTgp3m2/teMWOnH3hB/YNkDcJTjE7qFHtr8/cnL17j\nvddUbYxAXYQJ/zCgHFHYnnwXOLPQcnXD3Djnb6Smr0BeSTWKLp3DvDUHFI3FN3L+W6Qa9PYGDHxb\nDKNXwbGWzEFwKLhdYaLWn2PP19cq9mn7ohueUN6YCJp28/wcV/CONfMpC3dK9meqv4O60nF0tmQR\nlaiAWz707nM6oMbofcBXXuUFh8fCQPZbKnsxpDDhH0TOFVXiXJH8NZKAghBiyUlzuUxe+Sk1fYVl\nYe7eog8xX/0h+r+1Hnt0j2C99hnFhbV31V9I9ksqDUj/5YDoZ+4hzVCIphCDurJ105z2zSn0Ls6g\nFWdd4LuGKC/UxsPPMQwz/gbu998bkruUl5XJ2r7+dQUcLfm2aGA1tHHDX/Wr4AeAqmrv39gqjY5N\nI4KXZh9G4GHCP4h89P5/8MkHr8raCaUAOMtC5eHzJfh8k9QHWw0jNBBfQSepNmIUvwfJEF0im5Mi\nRQ0rhtRIPDnmrD2OpXvOeZU+YofucezUPS5p2y+0AZ6S+9Hrr+TK2jzlR+3riu0HdA96NV5FS3kx\nEwBiwfaWzoud+5tybWNs18kX9Qdytt+lVLg3sxH+cJHLxhtm/+59Ntgqg2Ph748I369HfO3zGL5y\na+s7Qz0Fv8OEf4D4cvMpWWGWDzTz8Z56AQDg8R/2YUuWjSsgIZbapNNUa/HO6mOSNA1Zumk4oZsu\nEeZ7dI9YtleZ/bVLb5Fq+1cRa4RrwpWDyNFNwpI/1nj9uY7nWTXWHtwpoIHcrv3xyn2ytqAz9TfJ\nbky0Z8FAgURlUtayH1Y5yT0TZ+NC23Wcn2cEvJp9F4r2/M+rc8sNjqNf/VHMpamThHzBIl7rf8eJ\nUMOEf4D4dOUePPv9dkXNJzV9BfYd2I8HvxLL8XEQRLOP2ebfmBQjRzcJpy7I/cR7E2Uf/jgqCmVT\nq6GS9s3apyzbKedF4TKO96AQuB2nzp2XN45biOMpd1h2O1R4rkX6KweMhTbXAbYl/JK8TNfsR4RE\ncVH5XMJAh33yShyYX2wjfR0UZveFeFKJuD8f8urcK9WOo2a9+bNW6qVrSgk6F6m43WDJ6CUAgEbo\n5dF5hmIxvmJGt4dd9Iw8Qib8CSGjCCHHCSHZhJD0UM0jEOSXVeOAbgaO6O5DaZV8cXSm6r/Yqn0S\nCzQfARBf8PUCtWQqrOWVL3/G6csVyL1ifa3+RfsfxWv+n9mfnQBAj0mKfVoJojnmUZX3vsvtiv6W\nN3YfD1Una4nAWapvcCC3WN7PCR+sOeG6kycQAtz9PdBhNHDrx8ANM/07vhdU3yO+ceXHOH4Qrdx1\nRPmARrlMoT9RE++SlxXXOBP+nkv/siqph42nKRyU6Nm4J9Qmz72jBGM8KOUQq/EtoC4cCYnwJ4Tw\nAOYBGA2gM4CJhJDOoZhLIPjnlHXhUjDK/d7vV60CAAzl9iPncgW0xIAjF8pkGTGXa2fiTH4Rhr6z\nXjaGI1Q8AcbMVTyWROW1ZD0lMc/61mBsfb1lu0mCtMbqlLlrZAnanHHm7yU+z02GLh6YtBTofY9Y\ncCXEWNJQOHGN/ESj/LcLZ0r1jh/03gj/EpvxFo5Y6NWclCFezEfM66PmWW4ff9EPQDal9BSlVA9g\nKQDvcxuEGTt27bBsG6uch4WfPCUW7HhY9YdiFGPrjNk4pZvi9rU1ai1g5+de+4OPplIvohqjCY//\nsA9nPPDOMdkkuNKPfM+yzdlpRgd0D2K1B3ljPo1Aoecplr9vAALKQkmp3vFvXPDC5l9ms4B8TbNr\nvJqTEgScF0FnYkpnf0UYhxOhEv4tANi6nOSa2yQQQmYQQjIIIRkFBcp5UsKRMznWmrxV1c4jXhf/\n5txv3dTYM/9zjUr+IzWZC6Ab7QK6Nx7Lh+Hgb7jpvVVuj/9NltXrJCrWmiKYqOUxv6ZTCiai+spE\nm3Qbdpp/RWO5HVoxieTwWbKFbI95/B8YH9/v2xh2+Fvzn79tsy/TcQLxWPgTIkCebKNuENYLvpTS\nBZTSPpTSPsnJyaGejtt8qf7Asn33584XV5do3nF6/Iraw88dZS4MMuFbS5PBKAqbcki182+++w7z\nNR/hsO5+p0Pa3sDdzEnoAIDorKYeTi23iaY0CryduuIGxymPw4LH/wEe3QN0HG3xzc/Ks2rKOVFd\ncKzfm+6NNfgpoO31rvs5I7EtVA1lepZPlBqcCH8vNP+tBeIDjgj+tbMTcF6bfeoioRL+5wG0tNlP\nMbdFHG+uPIrU9BW4WGI1qdgunL1nF2jlEAf5fFYc9DIv/9W3Yne7JwEAeoO4gBZlkrrk2frSuxv4\nNZq3Saussvqba3XyG7Vd9UG3p+uIksHOF2oNHfyfitmvJLYFkkUvn1rTQYreGsNRwidAVxXY2gsy\nbNc/xs4HjfKtkli5wYnZxwvNXxVjrs0Mfwt/zzV/EAFhriN7Tag+1R4A7QkhrQkhGgB3A4jI9Hln\ntv6Ij9VzMeot5ekP4g/j5d/cEIIvKwuAERW/ez+52mRg5lxBLTmr6azGKBX2nV/xrYCMUh6b8pY+\naqkAGnQc6vQ4500enxBBzA/44bw1DkJ085X3PVUQpIRiaRNBnjvtup8Tyo3OPLt8cOEV/Jt3UqAc\naowerrcQAZRGzm/ME0Ii/KlYjfkxAH8BOArgJ0qp/yt/B4EvNB9iDL8dG7VPO0w61iPjRdeuj7ba\nWGdrsWhtTZHbcxGmSh8UF8vEHDun8uWa2XXvbZLsv6Xy0atC5f+oUwBAy36yJqHdcMt2fFTovXjc\nRWnRsFPjKFQ26SNrP3wiW9YWMHxczKw2OXYY8CWxW4KfA/OqhWLoVac8OoeoSsGpAl8WNRSE7H2G\nUrqSUtqBUtqWUhrmhltlbCN0E0g5jl9Ufv0dr9qMijzrzVwxwUmO+VklwF2LUdRVtMN3Je5rZVyr\nAZL9koviD/2ZRfIkaS9UvifZv1u1yeG4bt+/vF3++kBk9ep4M7gx84Ah/yfux0TOWpCS8FffOgcq\nXn4bDkkMTfGQ7fYVxXzEExv7ofMlktxXfZv7N9kepykE4Zxr/m+uPIo5a8WYE0op1HEO4i7qAHXT\nmBUkfv71J8n+Z/OUc5oDgOmSNVGZKjHV5djlHcUQfo8Cb+xyvkwjoilnvfZZ2U14G78DvmBq2Fre\nONN327XAiZq8YdAzwDQFU9rE78VUB8NeER+U3uTuDxFEaV2nUStc3byBrLlf0wCbGm6eIwa/2bFp\n8wa/XsaTTNG3frYKQ963ep7d2vouv87FEasOXkRq+gpcKq3Gf3PS8eUJMeY0HDKSBpLIuXPCkPa8\nNAXvXM2nDvtu2G4tSq5uIPW2KOl2r6w/r1IwZ9y1BJtSnFRestMs97R9zLLd7iX33Tndgb9XOQ/N\n/hjb1AWe3z3cODH3kXrgI0Abu2Rs10Z2ILgjX3Gl4jPkeu9z0LtF3/vF4Dc7mp/yLr+PI9xdYBUE\nitgOsxHXcZaljQuSePpqx2FoktZh39kiqGJOQRUrxt7c8YVyWvG6AhP+PnB1krJ2tjtuGM4TaTKq\nZ1TWtwTOzj7eYOgjsEdR+Hceg/PxaZZdU28bF82n5SmQ4yutoRQ/quTZRH1CIaEbALRr5OMiXddx\nokYfkyg/dv0Lvo0dYhwJf7Vd3n9Tr3uc1gIIJPeovE/6p4S7Zp8ZP8odGxrHBSelwqHqxdAmr8Ps\njdKa1Mdo3Q48ZMLfBzQKRU0opeAEI0xEBWPHWyzt0cQm2IvjcfTuHbj0SLYo6JLlVaQ4B/VZtbAW\nSuGHvSxqby/lKVZ1atPJ+qDow7nOnUMdVBDLPHLM5bm1xIyyeci4+95c19+va7EX/s+aXT5rBX1y\nJ2DEbPAjI3IJTBF3zT5b8uTeZm0atvHzbJRRN8gEABRFf29p+++OHHAa39OhhDNM+PvA1QXycoYm\nkwBiFv6qsfNkx/dp+wKE4OpOndGksePFSsJJNf8znBgW0UVtUzUpOkG02yoEWAGA+hrnwVv2CArC\n/+CONej1c3/3B0npg3+GeOg5VMfSHThCpvnHJFm3nzwAPLgRGPgYUIfSB7vvVy9/K+K8qGXtDo7e\nRjiVNV35a1vmg1OXmvszV0+GHTmtxfJwedO2Wtr0hhrw1AATUQFRDUGfyZKc07NmD9zBaBdVSM03\nR5seYpri6sY9XA+iky8k2kPV1ihck0m6uHyxpAqVObtdX8fx6G71qq5xUUUqbQpwy0c+zCMCaNQq\nKJk7A0FjVXcAABUUTJVuv/0FL4rWvn6yErom1roZxMtsp+EOE/4+0LBEdAPjbbSbbTt3gaNGsc4q\nAOKlK6KsoLZZC9K2ugboORW6uxd5Na495P+OY2frR83XlGrg778zC9ccf0/pNBeDenYjr1+7wnmH\nsfOAPvJFcYb/KePiPT5HRxIBYzyUtHf342mDKPxZaUkATPj7RFyxuMjKC9b84zEXd4CjotkHgNcB\nNEa75F8J3c358nmVmLI5wU/2UF28JRJYMEmF/wea+b6N7abWp6685LpTHSMfvhcoCQQXojwveiNQ\nEwBeUXt3388/eKLI5CSlthKxCM7aQ7Bhwt8HPikS081eNMVZ2gaeeAc11VUQiG+Rpy0bSLNkxt8a\nuEXAihrx4VVtcH5THBdED5+qLhNdjOjZA+8qk+81fyOND3WPhnoKUh4VzXulqiQXHeUI1AQCDrbi\nZGYvMadVpaESJXbFWZRomSj1gJveMXDfj8mBY4MjUqPkEdh1ASb8faBnS9Gm3qSB1Fbbm8tClxrf\n6thyiXZBVAHMYTMoV1ygPbrFebrgP039gQfWI+r2T9wb2E2tr3m57wngIg2NJszSUiR3FN2TPdSK\nAcBEBbPwtz70jxeLHjSLT85G7zlzXI5xxWB1S+6V3B/P9A9c2UT7t2pXtEyIzLUYVzDh7wPNG4jp\nDDQ8jyvNr/Pv4EFIWLatxX0AAB0RNbNB/zyFC1cc5zExgQdS+riRx8czzd8UHTkpGvyFRh1mwh+A\nifDgqGeeV4JAUWXQg4A3PwBE9IJ1ET8q5Vun5p9Ptq+AXm2tTf3p8A8c9vUHRg81/5s69g7QTEIL\nE/6+UPuD5jgcbur/QmRFHcb7fUxbKJE/YJp/3BxFc29U7B9PPM006Z7mX9j6NgDAocF1O6jGlih1\n+N16Vwnn0bt8k1t9jSYB1336NbrMH4tSfi9MpAK2D31iJ1oyz8kTG56+XIE5a45j70VpcZl4jeeL\nzp7gqeZ/XcvrAjOREBN+v8AIgpi9BghHQEvcs1ubJv3i9vg0wDdBuV5ZA0q4vBsFeedk7Q+r/nBv\nYA/XuE0QH0KmmKYuetYdkrhy153CmPk7/0Zh/IeW3Pvgy+wWfKUP/nEL5EFc9/x3HeYfnIv88uB8\nF4JejBo/fik0SfPCDSb8faD2VZYQDlFE+rp8ud2diufwHYYrtiuhchDl6y+O5okmniuQP2T2Z8uF\nv6e4m8631sWUhCilQShI1QcxZXMA+HT3D7I2W22/fYMukmOqOHnG9rLYb6FN2oRcwzZL27Jb5IGT\n/kJfNBiAXPPXmloF7JrhDBP+vmAj/PXahpJDibf+R95/lmcaR/ywp72emj018amytu5XiZoQVVDV\nj57MkbVdut5x1lJb9p4SU10vWiMNEPu/n/YjNV3q03/ofAmo2cWU4+qP8G8VG75RzanpK3DTx87L\nj6piciT7VOBh+8rXJFqa+0nXVP7WaNCY04aorTUrVHwA17qoKO7k3j51M4LXFUz4+4JZ+HOEQ5Mm\nzaztL14AsU189nI+MNPzPOkkrilw72rAx0pLAFB0x0+ytu2nRTtscazcj1mTZc3aKVCCc20nocmQ\n+9y6VkzOXwCAJ/RfStp/+UdqGpv29W7c8ulWbDomZkcVfCwqEkmo1QEqfuMnjlwsdXq8R1JPyT7h\nTLAVJ97+JVUBSukAAEmxovt0rE4q7KlCxK+a1E0PH1uY8LdDECiy892zQVKLzR9oO+RuVA5/C3jx\nIqCxq0Ck0kordXlCqwFiDh8vqbrhdejjU6HRyvP/RGtFAZSvlb/2PqSyauinaVO0nPq52x5IanNx\nEs4uknIAdxj/VomFbEoqDdhy4hJGc7twNFdMoLX15BW3xq8L5LdQXlQPB3J0k5Cjm+S0z+5jclMh\nsV3w9UD6C0br/RJIzf/6jk0AAGrZJaTmySd6PoWfbv0uYPMIF+rPe7ab3P7ZNuzPLcEfjw1GtxQX\nuXFqbdqEAwhB9GB5auZQEzX0CWDoE0iskgvWO3QZAABDm+FAoWMffyWzkDN0PAVMQDKxao+CQPGD\nRgxU0xu/QmmVHqd1UwAAeVQsID6gXWOPrhPJtIgPv1vvfcN4PKO25vOnlDquQaCY8ZKz2XJfr7Qt\nk6gOoIszb36rMNlp+rbJ5364+Qd0TeoasDmEE0zzt+N07nncz6/E/nOutVBisflHgLkiqhEwdZmk\nKbVSDK4afNMU5D521uGpJg9/JjqF+9dks/hbUmXAlRKr619TIn7XF0tdR4LWFZJ7jA71FGSo7ZwW\n/jhw0WFfbbJSoRNbzd+7e4IPYGW22rHlrp4UjZCGg9MP1hvBDzDhL+M19SLMVH+L/dvcqXwlagxc\nEAKy/ELbGxSbCSFISXL8liN4qPlnxg6StV2psNYzoJSivLpG1ocG0N4bdoShZ9OTKqlycNJN86cV\n78w+khFI4Go7cOa4FvvcPmKit3r02zNT/z6xC5oR0fPgSlGBi56w8faJAM1fgSqV65TPANCCeFbU\nYujQYbI2rc3zUaBASqzCA1Mh6IwRPOwf8m0KHJcx1JJGCq1Wwe3tPRHIAK/a+gCXSqUpxPVGE/JL\n9bL+X9z4BZaPVagjXUdgwt+OazjR/ewBlRuav42rZyRyyc1FR08je3unypOD6fVWk45AKfhSuZmJ\nRMobVB1ljla6ZtWkhyeJ4gAAG0ZJREFUVO6bX0sy1wdEsHcisAp/JZv/xuP5MJicp1YIpCLFm5WL\n53/NtLsohZJ/0sDmA9G6QWtZe10hMqVWALlARc+a7abOLvtS849dqQB3uPJzkjVboqnLuIBcQ6WT\nV6LafMxqPxYohSFKns+nW8vwTHNcX6jRSLV5Ijheg7GkcZbg3GRz7zd78On6LKd9Akntgi/hqjH1\nq12YtVx8uBEIQS0mEy4ETGoRQmYRQs4TQjLN/26yOfYCISSbEHKcEDIyUHPwhuZms89xmoLU9BUw\nOtNUzF4DkaT53zTjNct22wZ2N+/ItwCVH4pmmyuIHRGsLqTZZ60+/oKJwmSSBzk1jNXJ2uoczXoA\nAXro+kqiSmoOqaqRm0JqEahREtF7VVwqQKz3CudAg/8x4xxWHXS8kBxICsrFzxOdOh9bsi5j0fYc\nLNxyyqz5R8497C8C/Yk/pJSmmf+tBABCSGcAdwPoAmAUgM8ICT9jbzfuNLqSU9h5qshhH2Lr6hkh\nRGtUwO0LxJ32dmafAY8AL+f55ToZQgcUUesbgOrMZsu2iQqywjEAQOpDhO9Dm4Hx34R6FoqMK5em\nbDhfWOagJ3DuSjmMJg5zbxCT8T3e61HwnPMF39gOr6JAn4V/ffcPAIDS4N431QbpmwkfdRpvrM6A\n+MbCNP9gMAbAUkppDaX0NIBsAP1CMA+nPKb6HX9qX8bOA0ccd6r1F460Bd8eE8RUEwG0sZvAgbcx\nA0RFWwN5dp3MBzXKhb+pHt6A4cSGxtMk+/05J799IgCUw7Utr8XeKXsxKnUUOGp9eCsVXyd8DWJa\nf4bo1mI9Zo2hg38m7iZlRmvFOHXD3YhO/QLRLb8GmNknIDxGCDlACPmaEIt7QAsAtlnDcs1tMggh\nMwghGYSQjIICN7xvAkBq8XYnR2s1/7r9w6nmPDcFtSCXMYC3Co+0NGs1JKPRKKsXDDg2FTCCg2BX\nV6Et59g8o26QCU4jvhVreDFSXAVrfitnC7e8Tny7rNIHN7+RAOv1dM1+BQBwugsOF3zrOj4Jf0LI\nOkLIIYV/YwB8DqAtgDQAFwF4XKGBUrqAUtqHUtonOTk0BT/6ck4WqCiFUA80hoKk/h6fk0LEXEa1\n3h3ERtgPIIcVzT6RYzyrm2i08jWXbdnu56RKFR60bHNuCFPCV7vs40/sawyIbYDoulH372N7fLrf\nKKXDKaVdFf79Tim9RCk1UTFr0pewmnbOA2hpM0yKuS0siVfLF3zLqg14/c8j2HumyM1yJRHGfX8B\nI6w1g1NSPS/qXUtRhbjIRm09R4xVoAqavzoMq1vVJ4zx8hxPkxfucvv8D+8cgi66Cfh6xDduvQzz\nUcGt3awk/CkIQChaNoxROKNuE0hvH5s0l7gdwCHz9nIAdxNCtISQ1gDaA9htf364QExyj4f5aw8i\nfue7uJ3fCj6AEYkh46r+wMDHgMZiTnYy9DmvhzJcMpfnM9kIf5MRGafkZjxNjHtBZ4zAEKPz7eHb\nrEEUlk54GX2b9VEUtKFGUfMnAgABDaK0wZ9QiAmke8W7hJA0iIbxHAAPAQCl9DAh5CcARwAYATxK\nKfW8anSAuEJj0YjYhLUraKi6XR/jcZXzYud1gmm/AWd3ALHem9yKKg1IAfDrnhwMNLcJJiNWZ57G\n9PDOalzv0Kjl4iAe3lXZ4rjwM6NEaZQdHDhVRUS5a/uLgAl/SulUJ8dmA5jt6HgoKKrQI06nkgp+\nAMQot0t2JL5XuYoIYhsDnX2rTRyrEYVAQWklYBb2VDDhdVV4ujvWZ5IU4ix2ax8FMEHS5qwYey3E\nDRu6qboZeF3wfP6j1Y61e3fmW9eof487BQSBotfra3HXFztkxzijPLXBCH5vMKYV0ZzqNAMAYDKI\n5h6VjaeFIJjQjrsQknkxHBPF22RepWIxEx2RR/lWG1y/qLvjuSXUBNeJQ8U5NmuFo5kq0NS/T6zA\nmyuP4hpyFDlnFfLN2Gn+Jwsiu/B2sChPFis9UbOtfyh3wHJMabGXEXraJFkXPT833mbZzi+T3gOl\nNa5zPbnlthtkZduZgC+urH+/yXoQUumar7aexGnd67hC5Tlp7DX/eRuyMSdYE4tkzL7fglFcMJ+u\nsmaIjI5nOXzCEV1jaxIz2xoO54qq0DjOahJatt918Xl3ErQ1jdfCs3yxvuFsSmcKq4I3kTCh3gv/\n8hojbuD2AYDF3p8fJyZ1a1x2BFUVpbCt5klo/dMQvIE3l62srJbfVMerGkDuVMgIJ0w2SduIvhyA\nNenbxfJ8l+e7U8mLgkJlaopvbnkPRsGIKLUf8ko5wdnjiNMF1+00HKj3Zp/Vh/LQkUj/8Nuib8BJ\nTUcAAG+UCq8bLtf92p7+4GiBaCpYslWuJc7fGLrMjgz3sM3tf/LAVsmxAmGPy/Pd8fYxJ0RHWuM0\n9GnaB10Su3g4S/+hijkVsmuHinov/KsNJjyn/lHStje3AjoqVppqCGlyq4bVdvFo0/8I6PwilRqj\nGBwXWyH3jEohVh//8vh2QZsTw32SibXM5sVL0piMnk26AwB0vOPAKPe8Z4IbI9O9aWpQrxfu1Hvh\nH63hcUxoKWkbxv2Dphrl0PO8EmuxafxrB9B6aCCnF7F0MYgxfW+oFsqOfaKZZ9nO7XhPsKbE8IC+\n3HHLtq5aauZJjhLjNye1ecbh+e7maQqmi+WEbtehf8NJDuYRdomFA069F/4to/ToxEm10wRShrj+\n0xX78+a6vSWIBZq4LvhSX2nZ52YAQHbK7diS5TgpX2xUPcjhH4E0sgnu2lsoXRoUzNlseeJ4ydCt\noCk34gX8Tes45QLt7WMGBHkmoafeC/9GtFjWpuYojK2GAABmGybBaBKQmr4C/92RA43ZX70CgV2c\ninRogxQAwLri5pj6leP8MClp8nq/jNBzjNq8DdvJaGopYuRYa3dl8s8vrbZUwgsml0prFNv7X9Up\nyDMJPfVe+Guq5J4LyXE6xEWLwr1XShyO5Yl2/5m/H8ZNvJiGqEbN8tA4gzMXZrl4pRxzm6xw3DGu\nOfDEPuDpY0GaGcMdat9wAbllXrAs1ToWH67MPttOXkY4FFF5JE2sWzzx6jtCOo9QUO+FP6cvlbUl\njUoHZw4Fb6Ch0JsExKESnM0NYSQsA6UziNnVUwUTbin53nFHjgcS2gDxzRz3YQSdoR0aW7b/pZI6\nNQiCuXa1EwHv6MGgLxoESjmLrT/YaRUSY6Rmxoe6P4SD0w8iJS4lqPMIB+q9n/+BfBPs/+wkdQjA\n8TBQHtkXCvH9T7txUPcAvjVaTRRC+FWeDCuouUrYTPW3zjuy7zGsuDJ8DtSNWkDbuD0w708AQE9O\n6q5L4bp2tUNXT6IHIQL2514Oidnnhqsb4X82mUWUKo7VF+rvJzfzwU6FOqXmAuQ1UENfU4mSy2L5\ntymq9ZYuTPg7R+Vubn6u3v8Ew4pGg+9HbJdRII1SnfSqNfs4sfk7OKZpJMYIbLqwyjJKMOnXrC96\nJdW/xV0l6v2dd8dVNq6bba4T/+fFFyI9VNDAiK7cadl5lAl/p8RHsQXxSIbYPJRLabTkWK23j7Nq\nXa48PQ202pwgLrjCX8Nr8MXIT4J6zXCl3gv/XlU2mTwn/wK8aE0xWwMNtDDgS41SNp96/9U5h6/3\nFsWIxtZqs0u4WnKMWkpXOxP+zu+P/NJKVNYYYRLklfICjU6lw4hm06BB/XbaqLcSbEtWAfafK8bx\nIpsfH68CNFYtR09V0CiktAWABjHMP90V57QdJPuVN80N0UwYnkIIwSZTDwDAjfxelFZb74Nam78z\ne7nLIC9iAgiF3hiaSngfjHgWe6dvdd2xDlMvhT+lFFO/2o0x87bhgNDWYT891NBAWfg37+tbkZP6\nwGGhJc7TRMt+dD+H9X0YYUjuaGvBnSHvbLRsC5bgLM9t/rXwsSfAR51xOgYjsNRL4b/9ZCGu5/Zh\nIHcIH2jmAwA2oq+sXw3U0CoJ/2dPgfR7MNDTjHiiqgvQghRir+B9AXhG6Jgy0KoYta0+bNmuFf5O\nNX8HC/mUisJeFZ0DwlcHvYg7w0q9NMw+vnAt/tG9J2nr+cxyWT89VIiCXQH3GZuAmERZX4aca3mx\ngEtvjmXxjHTG8VsAPAXA1uzjzM/fir5wCDSJW2Ao6QlV3AHR5MMIOfVS85/Ib5C1NYyNlrXVUA3i\niLWYy+H7soDmPQM6t7rEBcqKttQVihBn2Xarhq+N9G8u3IGK04+g+sIEoB4mUAtX6o3wp5Sif/oS\n9E9fgrZ29WPn8PcrnqOHCt3Nbp6zDNPQ5arGiv0YyrxnmOC6EyMikKR7sLh6OhYftjEAK5+4Dp+P\nvx1HXhsJamIuwOFCvRH+lXoTduoex07d44iGNLlTYo28di8AiX9/DVg6B08ZdPVVsrZ+1fMUejLC\nlQHVnwIQ34JrqX0MOC3VaHMoSsNjZJemiNaoUJU7OQCzZHiDT8KfEDKeEHKYECIQQvrYHXuBEJJN\nCDlOCBlp0z7K3JZNCEn35fqewNs4LtsWqgCAGlWcfXcAQCKxRv9W2/z4Ge5xpkS+WP7c+Oss22Nr\nXgvibBjecBGJqKFqRBPb+hbm2Fxnst9RdgdTI+UDjKDjq+Z/CMA4AJttGwkhnQHcDaALgFEAPiOE\n8IQQHsA8AKMBdAYw0dw34Nj+GO0XIJP6jnd5/m19Wrvsw5By4II1evotw0QAwJ29rZmUMimr4hXu\nHHt9FCqglbwtu+PqWXvEUNxb0q5yUgOAEVx8+ktQSo8Ciq9/YwAspZTWADhNCMkG0M98LJtSesp8\n3lJz3yO+zMMpf70EXNgHNe9Yc+/e6xrF9lyahBRyGQDQIE757YDhmIYxWtR6yprs9IwiGotnRnRQ\nOIsRTujUPPQQME21Flg3CwKl2JYn6npObf6EoOzobNg/IG7t1hp/VSqfwwgugbL5twBgWx4r19zm\nqD0wmAxAzhbgzDZwpzbKDp+niXjXcBfaNVN23byuZg4WGkcDAKoT6l+xB18pSehu2c60CabrWr0Q\ng2o+wWM3MP//SGCVqR9MlADb54LbMQ+FNXlIrtFayjkqIYp8HlqVVL+cfE0byX71xXH+nzDDLVxq\n/oSQdQCaKhx6iVL6u/+nJLn2DAAzAOCqq+SLhy7h1cBDopZSYzSh48urFbs95+B0I1R4wzgVbxin\n4n0wTx9PuSJEI7Vansu/HNHo0CQ2BDNieMPzxhl43jgDOW+LpTmfPX4e93yTidjhjnPj1BoD7J1C\n/7+9cw+2qqrj+OfLffISeUkIxEsyRYvwSmhopjggOj6aHvqHaWqm4kxmZTpWY2NOVlOa2uhgkmKZ\nr7IctTEtJ+2heFUU0NQLUsGQiKhoGQL31x973cu+h3POPfe89jln/z4ze+7aa+2915ff3ufH2uu3\n9lpNg8TbL3y356jyi3UKpl/nb2bzi7juBiC+KvrEkEee/Gx1LwGWAHR0dFRkEpBbz5yTs6zzG/Pp\n+M7DAKxc/2af/mqnf4a0ZB/T/ffLFxa8wLdTe7Q2RQsd5buDuaZ7nrHX8H7OdKpFpbp97gVOltQm\naSowA1gOPAnMkDRVUitRUHj3T2uryGEzxuYsGzOsrTfd2pyaUbFlY+607N1p7S1Nbs86ZPvObsyM\nHd0D+Mgr41BfvqF2KCngK+kk4FpgLHC/pBVmtsDMVku6kyiQuwNYbGY7wznnAw8Sfeq31MxW57h8\nxRnaWvjXhp89eFL/Bzl9mL7X0N70V4724G6907nuDU658fHe/bzj/HOQ+UZw0cJ9S9blFEepo33u\nAe7JUXYFcEWW/AeAB0qpt1x887jCR5k2e5NlwBx74HjO5xkAhrf7EL96Z+Nb7xZ87K4+/75N/8zV\nHc+a1zcA7FSPVHu0k+cUHkQeO7yt/4OcPsRbhtc90pXnSKceuPDOZ/vs5//AN3th5tuCd/8lh1u+\nQIa2ecu1FOLxE6cxKKTTJ3MOuFzrujvVJzXOv4CJCB3HKRM5p3fwUV41Q+qas2OGtbLX8HaWnr77\n4i3ZuGD+DJ7+55v9H+g4KaMQP97m3To1S8M7/1NveoKt727nji8eAsAZ86Zy3hGFzylzwXwfpeKk\nm6Wnd3DGzZ0DOqelaRBfW7AvR+8/rkKqnFJpeOf/2Mubk5bg4N1u9czhOb+Fyd/0X/wJn7ivlvF3\nMqcqZA75c+qH5iZ3E42I31Wnouw5JFoEZ94+ub+kduqTUmO3J8zauzxCnKJw5+9UlA9P3BOAw2aM\nSViJU25KHbdz1WdmlUWHUxypcf6FzEfilJ9eq/sIPyeDQT7oP1EaPuDbw6at/+v/IKfsWIj0+s+8\n8Sh2zP7glibOnOcr4yVNapz/th3d/R/kVAz/uMfp4YXLFyYtwSFF3T47Q7fPjp3e/eM45cD/O69v\nUuP8j7v2zwD86KGXElaSTtxRNB7+MlffpMb5O8ngH3c1BgdPGZm0BKfMuPN3KkrPx13eSqxvZk/e\n3fnnmrbZqQ9S5/wnjx6StIRU0dPyd0fRePhX2/VN6pz/4gFM6uaUTq/zd99f18S772ZNij7c829n\n6pvUOf+DvO+yqvR2+ySswymNze9s6023NEV3c7sPn65rUuX8W5rE9LHDkpaRKqaMjhZx32NwS8JK\nnFIYFHt1e3LdGwCs3fyfpOQ4ZaDhnf+yM+b0prf7GP+qc9nxM7nptA4OmDAiaSlOCYwe2rpb3pPr\ntiSgxCkXDe/83d0nS3tLE0ft5wt61DujgvM//dApvXl7DW9PSI1TDhre+Xd7UMpxSqbnV9TaPIjb\nzvooAOd+fHpygpySKcn5S/q0pNWSuiV1xPKnSHpX0oqw3RArO0jSSkldkq5RhSd98REJjlM6u4bs\nwqH7jGHdlccyYojHceqZUlv+q4BPAo9mKVtjZrPCdk4s/3rgC8CMsFV0lqf/vrejkpd3nFQwrD2a\nA3J4e2rmgmx4SrqTZvYCFD5jo6TxwB5m9njYXwacCPyuFB35+Nua13vTI3zEieMUxSkHT+K9Hd2c\nOndy0lKcMlHJPv+pkp6R9CdJh4W8CcD62DHrQ15WJJ0tqVNS52uvvVaUiOamXf8x/eXiI4u6huOk\nneamQZw5byqtzQ0fJkwN/bb8JT0MvC9L0aVm9tscp20E3m9mr0s6CPiNpJkDFWdmS4AlAB0dHUV1\n3h+wdzTEcMHMcQxr81dWx3EcKMD5m9n8gV7UzLYB20L6KUlrgA8AG4CJsUMnhryKMbi1CYCRQ3Yf\np+w4jpNWKtIUljQW2GJmOyVNIwrsrjWzLZK2SpoLPAF8Dri2Ehp6WHTgeJ7fuJXzfE4fx3GcXkod\n6nmSpPXAIcD9kh4MRYcDz0laAdwNnGNmPZ8Dngf8FOgC1lDBYC9AS9MgLjlmPw/2Oo7jxJDVyWob\nHR0d1tnZmbQMx3GcukHSU2bWka3MQ/eO4zgpxJ2/4zhOCnHn7ziOk0Lc+TuO46QQd/6O4zgpxJ2/\n4zhOCnHn7ziOk0LqZpy/pNeAfxR5+hhgcxnllINa1AS1qasWNUFt6qpFTVCbumpRE5RX12QzG5ut\noG6cfylI6sz1oUNS1KImqE1dtagJalNXLWqC2tRVi5qgerq828dxHCeFuPN3HMdJIWlx/kuSFpCF\nWtQEtamrFjVBbeqqRU1Qm7pqURNUSVcq+vwdx3GcvqSl5e84juPEcOfvOI6TQhra+UtaKOlFSV2S\nLq5SneskrZS0QlJnyBsl6SFJL4e/I0O+JF0T9D0naXbsOqeF41+WdNoANSyVtEnSqlhe2TRIOij8\nG7vCuSpB12WSNgR7rZC0KFZ2SajjRUkLYvlZ76ukqZKeCPl3SOp37U5JkyQ9Iul5SaslfSlpe+XR\nlLSt2iUtl/Rs0PXtfNeS1Bb2u0L5lGL1FqHpZkmvxGw1K+RX7XkP5zZJekbSfUnbajfMrCE3oIlo\npbBpQCvwLLB/FepdB4zJyPs+cHFIXwx8L6QXEa1kJmAu8ETIHwWsDX9HhvTIAWg4HJgNrKqEBmB5\nOFbh3GNK0HUZ8NUsx+4f7lkbMDXcy6Z89xW4Ezg5pG8Azi1A03hgdkgPB14KdSdmrzyakraVgGEh\n3UK0FOvcXNciWrXvhpA+GbijWL1FaLoZ+FSW46v2vIdzLwRuA+7LZ/dq2Cpza+SW/xygy8zWmtl7\nwO3ACQlpOQG4JaRvAU6M5S+ziMeBPSWNBxYAD5nZFjN7A3gIWFhoZWb2KLAlI7ssGkLZHmb2uEVP\n57LYtYrRlYsTgNvNbJuZvUK07OccctzX0Bo7kmjZ0Mx/Yz5NG83s6ZB+G3gBmECC9sqjKWlbmZm9\nE3ZbwmZ5rhW34d3AUaHuAektUlMuqva8S5oIHEu0bC392L3itsqkkZ3/BOBfsf315P8BlQsDfi/p\nKUlnh7xxZrYxpP8NjOtHYyW0l0vDhJAup7bzwyv4UoXulSJ0jQbeNLMdxeoKr9ofIWo91oS9MjRB\nwrYK3RgrgE1EDnJNnmv11h/K3wp1l/W5z9RkZj22uiLY6ipJbZmaCqy7lPt3NXAR0B3289m9KraK\n08jOPynmmdls4BhgsaTD44Wh9ZDo+Npa0BDjemA6MAvYCPwwCRGShgG/Ai4ws63xsqTslUVT4rYy\ns51mNguYSNT6/GC1NWSSqUnSAcAlRNoOJurK+Xo1NUk6DthkZk9Vs96B0MjOfwMwKbY/MeRVFDPb\nEP5uAu4h+oG8Gl4fCX839aOxEtrLpWFDSJdFm5m9Gn683cCNRPYqRtfrRK/wzQPVJamFyMn+wsx+\nHbITtVc2TbVgqx7M7E3gEeCQPNfqrT+Ujwh1V+S5j2laGLrOzMy2AT+jeFsV+7x/DDhe0jqiLpkj\ngR9TI7YCGjrg20wUtJnKroDIzArXORQYHkv/laiv/gf0DR5+P6SPpW/wabntCj69QhR4GhnSowao\nZQp9A6tl08DuAbBFJegaH0t/mah/E2AmfQNda4mCXDnvK3AXfYNp5xWgR0T9uFdn5CdmrzyakrbV\nWGDPkB4MPAYcl+tawGL6BjHvLFZvEZrGx2x5NXBlEs97OP8IdgV8E7PVbroGcnC9bUSR/ZeI+iUv\nrUJ908JNeBZY3VMnUd/dH4CXgYdjD5WAnwR9K4GO2LXOIArudAGfH6COXxJ1C2wn6gs8s5wagA5g\nVTjnOsKX4kXqujXU+xxwL30d3KWhjheJjbDIdV+D/ZcHvXcBbQVomkfUpfMcsCJsi5K0Vx5NSdvq\nQ8Azof5VwLfyXQtoD/tdoXxasXqL0PTHYKtVwM/ZNSKoas977Pwj2OX8E7NV5ubTOziO46SQRu7z\ndxzHcXLgzt9xHCeFuPN3HMdJIe78HcdxUog7f8dxnBTizt9xHCeFuPN3HMdJIf8HeiDuU6Js0psA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epochs:  20  Iterations:  4662  Loss:  13.643715054870725\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wabZqWVRh5p8abzf1mMx29fbcJmkEti/srF4ra7u7YDIAICuum0WW6DOihEvi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epochs:  30  Iterations:  6882  Loss:  6.973975184532973\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kAviVECIxBSillBDPaG3nsRkAZgBAfn5+PES0MQLA2iBavFtzHkOe27jBIFbT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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epochs:  40  Iterations:  9102  Loss:  7.226891136921204\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MYiqoLkYqanz3izBXffEkHt56Bf4sLNB13D1ntsPgsLopBWo93D68W6humxTp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\ngiAoIf8HN80OJGFFDM4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7Az-_f74IXw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}